Predicting Synergy in Natural Product Combinations: Network Proximity, Pathway Complementarity, Dose Logic, and Safety Constraints

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Natural product combinations remain important in phytopharmacology and systems pharmacology because they may contain multiple bioactive constituents capable of acting across targets, pathways, and phenotypes. However, synergy claims are often vulnerable to overinterpretation when co-activity, multi-target exposure, traditional co-use, or pathway overlap is treated as evidence of true pharmacological interaction. This article proposes an original computational framework for predicting and prioritizing potential synergy in natural product combinations by integrating combination identity, compound or extract characterization, target mapping, disease-network context, network proximity, target overlap, target separation, pathway complementarity, pathway redundancy, dose-response logic, exposure plausibility, interaction risk, toxicity constraints, evidence grading, uncertainty communication, and validation planning. The framework distinguishes predicted synergy from observed in vitro synergy, additivity, antagonism, mechanistic complementarity, pharmacological plausibility, and clinical usefulness. It emphasizes that network proximity can support disease-contextual prioritization, whereas pathway complementarity can identify biologically coherent but non-confirmatory hypotheses. Dose logic and safety constraints are positioned as essential filters before candidate prioritization, particularly because pharmacokinetic interactions, pharmacodynamic interactions, toxicity risk, and herb–drug interaction risk may alter interpretation. The main contribution is a structured computational decision framework that treats synergy prediction as hypothesis generation rather than validation, thereby supporting more cautious, transparent, and experimentally testable prioritization of natural product combinations.